Genetic Architecture Of Traits Research Articles

Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models

The prediction accuracy of multi-environment prediction models can be affected by the complexity of the genotype by environment interaction (G×E). Moreover, depending on the trait genetic architecture, accounting for non-additive effects, such as dominance effects, may increase the prediction accuracy of genomic models. Hence, we aimed to verify empirically: (i) the impact of the genotype by environment complexity on the prediction accuracy of grain yield in maize hybrids; (ii) the advantage of dominance effects modeling for the prediction of maize hybrids in multi-environment trials; (iii) how parent information impacts on the prediction accuracy of hybrids in multi-environment genomic models. We used a dataset comprising 614 maize hybrids evaluated during two growing seasons, under two nitrogens regimes at two locations in Brazil. The prediction accuracies were obtained using four different validation systems (hybrids and half-sib families based sampling). Our results suggest that sampling entire half-sib families or individual hybrids can achieve similar accuracy estimates in multi-environment prediction models. Moreover, modeling dominance deviations in a multi-environment prediction model can significantly increase the prediction accuracy, mainly under high G×E complexity. Also, we found a linear relationship between prediction accuracy and G×E complexity. Furthermore, we observed significant increases in prediction accuracy of lowly correlated environments when information of a linking trial/environment was included in the prediction model.

Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used Genomic Structural Equation Modeling and prior genome-wide association studies (GWAS) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability.We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance but showed different associations with gray-matter brain volumes. Non-cognitive genetics were further distinguished by associations with personality traits, less risky behavior,and increased risk for certain psychiatric disorders.For socioeconomic success and longevity, non-cognitive and cognitive-performance genetics demonstrated similar-magnitude associations. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

High-Resolution Linkage Map and QTL Analyses of Fruit Firmness in Autotetraploid Blueberry.

Blueberry (Vaccinium corymbosum and hybrids) is an autotetraploid crop whose commercial relevance has been growing steadily during the last 20 years. However, the ever-increasing cost of labor for hand-picking blueberry is one main constraint in competitive marketing of the fruit. Machine harvestability is, therefore, a key trait for the blueberry industry. Understanding the genetic architecture of traits related to machine harvestability through Quantitative Trait Loci (QTL) mapping is the first step toward implementation of molecular breeding for faster genetic gains. Despite recent advances in software development for autotetraploid genetic mapping, a high-resolution map is still not available for blueberry. In this study, we crafted a map for autotetraploid low-chill highbush blueberry containing 11,292 SNP markers and a total size of 1,953.97 cM (average density of 5.78 markers/cM). This map was subsequently used to perform QTL analyses in 2-year field trials for a trait crucial to machine harvesting: fruit firmness. Preliminary insights were also sought for single evaluations of firmness retention after cold storage, and fruit detachment force traits. Significant QTL peaks were identified for all the traits and overlapping QTL intervals were detected for firmness across the years. We found low-to-moderate QTL effects explaining the phenotypic variance, which suggest a quantitative nature of these traits. The QTL intervals were further speculated for putative gene repertoire. Altogether, our findings provide the basis for future fine-mapping and molecular breeding efforts for machine harvesting in blueberry.

Using Local Convolutional Neural Networks for Genomic Prediction.

The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular, local convolutional neural networks (LCNN) for genomic prediction, as a region-specific filter corresponds much better with our prior genetic knowledge on the genetic architecture of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000; p = 34,595) and real Arabidopsis data (n = 2,039; p = 180,000) for a variety of traits based on their predictive ability. The baseline LCNN, containing one local convolutional layer (kernel size: 10) and two fully connected layers with 64 nodes each, is outperforming commonly proposed ANNs (multi layer perceptrons and convolutional neural networks) for basically all considered traits. For traits with high heritability and large training population as present in the simulated data, LCNN are even outperforming state-of-the-art methods like genomic best linear unbiased prediction (GBLUP), Bayesian models and extended GBLUP, indicated by an increase in predictive ability of up to 24%. However, for small training populations, these state-of-the-art methods outperform all considered ANNs. Nevertheless, the LCNN still outperforms all other considered ANNs by around 10%. Minor improvements to the tested baseline network architecture of the LCNN were obtained by increasing the kernel size and of reducing the stride, whereas the number of subsequent fully connected layers and their node sizes had neglectable impact. Although gains in predictive ability were obtained for large scale data sets by using LCNNs, the practical use of ANNs comes with additional problems, such as the need of genotyping all considered individuals, the lack of estimation of heritability and reliability. Furthermore, breeding values are additive by design, whereas ANN-based estimates are not. However, ANNs also comes with new opportunities, as networks can easily be extended to account for additional inputs (omics, weather etc.) and outputs (multi-trait models), and computing time increases linearly with the number of individuals. With advances in high-throughput phenotyping and cheaper genotyping, ANNs can become a valid alternative for genomic prediction.

Contrasting signatures of genomic divergence during sympatric speciation

The transition from ‘well-marked varieties’ of a single species into ‘well-defined species’—especially in the absence of geographic barriers to gene flow (sympatric speciation)—has puzzled evolutionary biologists ever since Darwin1,2. Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process3. Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs4, but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories5. Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.

A Nested Association Mapping Panel in Arabidopsis thaliana for Mapping and Characterizing Genetic Architecture.

Linkage and association mapping populations are crucial public resources that facilitate the characterization of trait genetic architecture in natural and agricultural systems. We define a large nested association mapping panel (NAM) from 14 publicly available recombinant inbred line populations (RILs) of Arabidopsis thaliana, which share a common recurrent parent (Col-0). Using a genotype-by-sequencing approach (GBS), we identified single nucleotide polymorphisms (SNPs; range 563-1525 per population) and subsequently built updated linkage maps in each of the 14 RIL sets. Simulations in individual RIL populations indicate that our GBS markers have improved power to detect small effect QTL and enhanced resolution of QTL support intervals in comparison to original linkage maps. Using these robust linkage maps, we imputed a common set of publicly available parental SNPs into each RIL linkage map, generating overlapping markers across all populations. Though ultimately depending on allele frequencies at causal loci, simulations of the NAM panel suggest that surveying between 4 to 7 of the 14 RIL populations provides high resolution of the genetic architecture of complex traits, relative to a single mapping population.

A single pleiotropic locus influences the rate of hybridization between two sibling species of Lygaeus bugs.

The evolution of reproductive isolation lies at the heart of understanding the process of speciation. Of particular interest is the relationship between pre‐ and postzygotic reproductive isolation, and the genetic architecture of traits that contribute to one or both forms of reproductive isolation. The sibling species of seed bug Lygaeus equestris and L. simulans show a classic pattern of asymmetric prezygotic reproductive isolation, with female L. equestris hybridizing with male L. simulans, but with no hybridization in the reciprocal direction. We have recently described a mutant pale color form of L. simulans, that inherits as a single Mendelian locus and is pleiotropic for a number of other life history and behavioral traits. Here, we tested whether this locus also influences pre‐ and postzygotic reproductive isolation. Two sets of experimental crosses revealed that behavioral isolation varied with mutant versus wild‐type phenotype for male L. simulans, with the pale form less successful at mating with female L. equestris. In terms of trying to assess postzygotic isolation, levels of hybrid offspring production were uniformly low across the experiments. However, we did obtain, for the first time, hybrid offspring from a pairing between a female L. simulans and a male L. equestris. In this instance, the female was of the pale mutant genotype. Together with evidence for heterozygote advantage in terms of nymph survival, we consider our results in terms of possible mechanisms of reproductive isolation between this species pair, the role of the pale mutation, and the possible genetic architectures underlying the mutation, from a single gene to a supergene.

Genetic architecture and genomic selection of female reproduction traits in rainbow trout

BackgroundRainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits.ResultsA pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits.ConclusionsOur genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

Validation of an Associative Transcriptomics platform in the polyploid crop species Brassica juncea by dissection of the genetic architecture of agronomic and quality traits

The development of more productive crops will be key to addressing the challenges that climate change, population growth and diminishing resources pose to global food security. Advanced 'omics techniques can help to accelerate breeding by facilitating the identification of genetic markers for use in marker-assisted selection. Here, we present the validation of a new Associative Transcriptomics platform in the important oilseed crop Brassica juncea. To develop this platform, we established a pan-transcriptome reference for B. juncea, to which we mapped transcriptome data from a diverse panel of B. juncea accessions. From this panel, we identified 355050 single nucleotide polymorphism variants and quantified the abundance of 93963 transcripts. Subsequent association analysis of functional genotypes against a number of important agronomic and quality traits revealed a promising candidate gene for seed weight, BjA.TTL, as well as additional markers linked to seed colour and vitamin E content. The establishment of the first full-scale Associative Transcriptomics platform for B. juncea enables rapid progress to be made towards an understanding of the genetic architecture of trait variation in this important species, and provides an exemplar for other crops.

Editorial: Polygenic scores in child and adolescent psychiatry – strengths, weaknesses, opportunities and threats

Polygenic scores estimate an individual's genetic liability for a particular disorder or trait. They are based on current knowledge of the trait's genetic architecture and focus on common genetic variants. In this editorial, I will discuss some of the strengths, weaknesses, opportunities and threats (SWOT) to polygenic scores within the context of child and adolescent psychiatry. I consider how the potential application of polygenic scores in health settings has some parallels with existing practices, but that polygenic scores also undoubtedly raise unique challenges. This SWOT analysis is accompanied by discussion of some new findings using polygenic scores in this issue of Journal of Child Psychology and Psychiatry.

The genetics of evolutionary radiations.

With the realization that much of the biological diversity on Earth has been generated by discrete evolutionary radiations, there has been a rapid increase in research into the biotic (key innovations) and abiotic (key environments) circumstances in which such radiations took place. Here we focus on the potential importance of population genetic structure and trait genetic architecture in explaining radiations. We propose a verbal model describing the stages of an evolutionary radiation: first invading a suitable adaptive zone and expanding both spatially and ecologically through this zone; secondly, diverging genetically into numerous distinct populations; and, finally, speciating. There are numerous examples of the first stage; the difficulty, however, is explaining how genetic diversification can take place from the establishment of a, presumably, genetically depauperate population in a new adaptive zone. We explore the potential roles of epigenetics and transposable elements (TEs), of neutral process such as genetic drift in combination with trait genetic architecture, of gene flow limitation through isolation by distance (IBD), isolation by ecology and isolation by colonization, the possible role of intra-specific competition, and that of admixture and hybridization in increasing the genetic diversity of the founding populations. We show that many of the predictions of this model are corroborated. Most radiations occur in complex adaptive zones, which facilitate the establishment of many small populations exposed to genetic drift and divergent selection. We also show that many radiations (especially those resulting from long-distance dispersal) were established by polyploid lineages, and that many radiating lineages have small genome sizes. However, there are several other predictions which are not (yet) possible to test: that epigenetics has played a role in radiations, that radiations occur more frequently in clades with small gene flow distances, or that the ancestors of radiations had large fundamental niches. At least some of these may be testable in the future as more genome and epigenome data become available. The implication of this model is that many radiations may be hard polytomies because the genetic divergence leading to speciation happens within a very short time, and that the divergence history may be further obscured by hybridization. Furthermore, it suggests that only lineages with the appropriate genetic architecture will be able to radiate, and that such a radiation will happen in a meta-population environment. Understanding the genetic architecture of a lineage may be an essential part of accounting for why some lineages radiate, and some do not.

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.

Evaluation of agronomic performance and exploratory genome‐wide association study of a diverse oat panel for forage use in Taiwan

AbstractOats (Avena sativa L.) produce not only the well‐known highly nutritious grain, but valuable forage. In Taiwan, imported oat hay is a popular forage with the dairy cattle industry. However, local forage oat breeding and production have been virtually absent. Therefore, this study aimed to evaluate the forage performance of oats in Taiwan in order to assess their potential for local oat production. We have introduced 178 oat accessions from 18 breeding programs across North and South America. These breeding lines were evaluated in Northern Taiwan (Taipei) and Southern Taiwan (Hengchun) between autumn 2016 and spring 2017. Early maturing lines reached the flowering stage after ca. 70 or ca. 90 days after sowing in Hengchun and Taipei, respectively, while 49 and 159 lines remained vegetative throughout the growing season in Hengchun and Taipei, respectively. On average, plant height was taller in Taipei than in Hengchun (99.8 vs. 53.2 cm), while dry matter yield per plot was higher in Hengchun than in Taipei (596 vs. 388.4 g/Plot). Maturity and dry matter differences may be related to the differences in daily effective sunlight between locations. Using 1,164 genome‐wide single nucleotide polymorphisms (SNPs), we explored the population structure and the genetic architecture of traits of interest within a subset of 73 oat lines. No obvious sub‐populations were observed, which reflected the frequent material exchanges among oat breeders. Fifty‐three loci were associated with days to flowering, plant height and dry matter yield; and 12 of them overlapped with loci identified in previous work. The data and results of this study lay the foundations and provide guidelines for further forage oat breeding work in Taiwan.

High-Resolution Linkage Map With Allele Dosage Allows the Identification of Regions Governing Complex Traits and Apospory in Guinea Grass (Megathyrsus maximus).

Forage grasses are mainly used in animal feed to fatten cattle and dairy herds, and guinea grass (Megathyrsus maximus) is considered one of the most productive of the tropical forage crops that reproduce by seeds. Due to the recent process of domestication, this species has several genomic complexities, such as autotetraploidy and aposporous apomixis. Consequently, approaches that relate phenotypic and genotypic data are incipient. In this context, we built a linkage map with allele dosage and generated novel information of the genetic architecture of traits that are important for the breeding of M. maximus. From a full-sib progeny, a linkage map containing 858 single nucleotide polymorphism (SNP) markers with allele dosage information expected for an autotetraploid was obtained. The high genetic variability of the progeny allowed us to map 10 quantitative trait loci (QTLs) related to agronomic traits, such as regrowth capacity and total dry matter, and 36 QTLs related to nutritional quality, which were distributed among all homology groups (HGs). Various overlapping regions associated with the quantitative traits suggested QTL hotspots. In addition, we were able to map one locus that controls apospory (apo-locus) in HG II. A total of 55 different gene families involved in cellular metabolism and plant growth were identified from markers adjacent to the QTLs and APOSPORY locus using the Panicum virgatum genome as a reference in comparisons with the genomes of Arabidopsis thaliana and Oryza sativa. Our results provide a better understanding of the genetic basis of reproduction by apomixis and traits important for breeding programs that considerably influence animal productivity as well as the quality of meat and milk.

Unraveling the genetic architecture for carbon and nitrogen related traits and leaf hydraulic conductance in soybean using genome-wide association analyses

BackgroundDrought stress is a major limiting factor of soybean [Glycine max (L.) Merr.] production around the world. Soybean plants can ameliorate this stress with improved water-saving, sustained N2 fixation during water deficits, and/or limited leaf hydraulic conductance. In this study, carbon isotope composition (δ13C), which can relate to variation in water-saving capability, was measured. Additionally, nitrogen isotope composition (δ15N) and nitrogen concentration that relate to nitrogen fixation were evaluated. Decrease in transpiration rate (DTR) of de-rooted soybean shoots in a silver nitrate (AgNO3) solution compared to deionized water under high vapor pressure deficit (VPD) conditions was used as a surrogate measurement for limited leaf hydraulic conductance. A panel of over 200 genetically diverse soybean accessions genotyped with the SoySNP50K iSelect BeadChips was evaluated for the carbon and nitrogen related traits in two field environments (Athens, GA in 2015 and 2016) and for transpiration response to AgNO3 in a growth chamber. A multiple loci linear mixed model was implemented in FarmCPU to perform genome-wide association analyses for these traits.ResultsThirty two, 23, 26, and nine loci for δ13C, δ15N, nitrogen concentration, and transpiration response to AgNO3, respectively, were significantly associated with these traits. Candidate genes that relate to drought stress tolerance enhancement or response were identified near certain loci that could be targets for improving and understanding these traits. Soybean accessions with favorable breeding values were also identified. Low correlations were observed between many of the traits and the genetic loci associated with each trait were largely unique, indicating that these drought tolerance related traits are governed by different genetic loci.ConclusionsThe genomic regions and germplasm identified in this study can be used by breeders to understand the genetic architecture for these traits and to improve soybean drought tolerance. Phenotyping resources needed, trait heritability, and relationship to the target environment should be considered before deciding which of these traits to ultimately employ in a specific breeding program. Potential marker-assisted selection efforts could focus on loci which explain the greatest amount of phenotypic variation for each trait, but may be challenging due to the quantitative nature of these traits.

Endless forms of sexual selection.

In recent years, the field of sexual selection has exploded, with advances in theoretical and empirical research complementing each other in exciting ways. This perspective piece is the product of a “stock-taking” workshop on sexual selection and sexual conflict. Our aim is to identify and deliberate on outstanding questions and to stimulate discussion rather than provide a comprehensive overview of the entire field. These questions are organized into four thematic sections we deem essential to the field. First we focus on the evolution of mate choice and mating systems. Variation in mate quality can generate both competition and choice in the opposite sex, with implications for the evolution of mating systems. Limitations on mate choice may dictate the importance of direct vs. indirect benefits in mating decisions and consequently, mating systems, especially with regard to polyandry. Second, we focus on how sender and receiver mechanisms shape signal design. Mediation of honest signal content likely depends on integration of temporally variable social and physiological costs that are challenging to measure. We view the neuroethology of sensory and cognitive receiver biases as the main key to signal form and the ‘aesthetic sense’ proposed by Darwin. Since a receiver bias is sufficient to both initiate and drive ornament or armament exaggeration, without a genetically correlated or even coevolving receiver, this may be the appropriate ‘null model’ of sexual selection. Thirdly, we focus on the genetic architecture of sexually selected traits. Despite advances in modern molecular techniques, the number and identity of genes underlying performance, display and secondary sexual traits remains largely unknown. In-depth investigations into the genetic basis of sexual dimorphism in the context of long-term field studies will reveal constraints and trajectories of sexually selected trait evolution. Finally, we focus on sexual selection and conflict as drivers of speciation. Population divergence and speciation are often influenced by an interplay between sexual and natural selection. The extent to which sexual selection promotes or counteracts population divergence may vary depending on the genetic architecture of traits as well as the covariance between mating competition and local adaptation. Additionally, post-copulatory processes, such as selection against heterospecific sperm, may influence the importance of sexual selection in speciation. We propose that efforts to resolve these four themes can catalyze conceptual progress in the field of sexual selection, and we offer potential avenues of research to advance this progress.

Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses.

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations – an important breeding step – that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11–27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection

BackgroundNational genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from either California or New Mexico to construct and compare genomic prediction models. The sequence variation reference dataset comprised variants called for 1578 animals from Run 5 of the 1000 Bull Genomes Project, including 450 Holsteins and 29 animals sequenced from this study population. Genotypes for 9,282,726 variants with minor allele frequencies ≥5% were imputed and used to obtain genomic predictions in GEMMA using a Bayesian Sparse Linear Mixed Model.ResultsVariation explained by markers increased from 13.6% using BovineHD data to 14.4% using imputed whole genome sequence data and the resolution of genomic regions detected as harbouring QTL substantially increased. Explained variation in the analysis of the combined California and New Mexico data was less than when data for each state were separately analysed and the estimated genetic correlation between risk of Bovine Respiratory Disease in California and New Mexico Holsteins was − 0.36. Consequently, genomic predictions trained using the data from one state did not accurately predict disease risk in the other state. To determine if a prediction model could be developed with utility in both states, we selected variants within genomic regions harbouring: 1) genes involved in the normal immune response to infection by pathogens responsible for Bovine Respiratory Disease detected by RNA-Seq analysis, and/or 2) QTL identified in the association analysis of the imputed sequence variants. The model based on QTL selected variants is biased but when trained in one state generated BRD risk predictions with positive accuracies in the other state.ConclusionsWe demonstrate the utility of sequence-based and biology-driven model development for genomic selection. Disease phenotypes cannot be routinely recorded in most livestock species and the observed phenotypes may vary in their genomic architecture due to variation in the pathogen composition across environments. Elucidation of trait biology and genetic architecture may guide the development of prediction models with utility across breeds and environments.

Genetic Loci Associated with Salt Tolerance in Advanced Breeding Populations of Tetraploid Alfalfa Using Genome-Wide Association Studies.

Many agricultural lands in the western United States consist of soil with high concentrations of salt, which is detrimental to alfalfa ( L.) growth and production, especially in the region where water resource is limited. Developing alfalfa varieties with salt tolerance is imperative for sustainable production under increasing soil salinity. In the present study, we used advanced alfalfa breeding populations and evaluated five traits related to salt tolerance including biomass dry weight (DW) and fresh weight (FW), plant height (PH), leaf relative water content (RWC), and stomatal conductance (SC) under control and salt stress. Stress susceptibility index (SSI) of each trait and single-nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS) were used for genome-wide association studies (GWAS) to identify loci associated with salt tolerance. A total of 53 significant SNPs associated with salt tolerance were identified and they were located at 49 loci through eight chromosomes. A Basic Local Alignment Search Tool (BLAST) search of the regions surrounding the SNPs revealed 21 putative candidate genes associated with salt tolerance. The genetic architecture for traits related to salt tolerance characterized in this report could help in understanding the genetic mechanism by which salt stress affects plant growth and production in alfalfa. The markers and candidate genes identified in the present study would be useful for marker-assisted selection (MAS) in breeding salt-tolerant alfalfa after validation of the markers.